research-article

Falkon: a Fast and Light-weight tasK executiON framework

Authors:

Catalin Dumitrescu,

Mike WildeAuthors Info & Claims

SC '07: Proceedings of the 2007 ACM/IEEE conference on Supercomputing

Article No.: 43, Pages 1 - 12

https://doi.org/10.1145/1362622.1362680

Published: 10 November 2007 Publication History

Abstract

To enable the rapid execution of many tasks on compute clusters, we have developed Falkon, a Fast and Light-weight tasK executiON framework. Falkon integrates (1) multi-level scheduling to separate resource acquisition (via, e.g., requests to batch schedulers) from task dispatch, and (2) a streamlined dispatcher. Falkon's integration of multi-level scheduling and streamlined dispatchers delivers performance not provided by any other system. We describe Falkon architecture and implementation, and present performance results for both microbenchmarks and applications. Microbenchmarks show that Falkon throughput (487 tasks/sec) and scalability (to 54,000 executors and 2,000,000 tasks processed in just 112 minutes) are one to two orders of magnitude better than other systems used in production Grids. Large-scale astronomy and medical applications executed under Falkon by the Swift parallel programming system achieve up to 90% reduction in end-to-end run time, relative to versions that execute tasks via separate scheduler submissions.

References

[1]

D. Thain, T. Tannenbaum, and M. Livny, "Distributed Computing in Practice: The Condor Experience" Concurrency and Computation: Practice and Experience, Vol. 17, No. 2--4, pages 323--356, February-April, 2005.

Digital Library

[2]

Swift Workflow System: www.ci.uchicago.edu/swift, 2007.

[3]

Y. Zhao, M. Hategan, B. Clifford, I. Foster, G. von Laszewski, I. Raicu, T. Stef-Praun, M. Wilde. "Swift: Fast, Reliable, Loosely Coupled Parallel Computation", IEEE Workshop on Scientific Workflows 2007.

[4]

I. Foster, J. Voeckler, M. Wilde, Y. Zhao. "Chimera: A Virtual Data System for Representing, Querying, and Automating Data Derivation", SSDBM 2002.

Digital Library

[5]

J.-P Goux, S. Kulkarni, J. T. Linderoth, and M. E. Yoder, "An Enabling Framework for Master-Worker Applications on the Computational Grid," IEEE International Symposium on High Performance Distributed Computing, 2000.

Digital Library

[6]

I. Foster, C. Kesselman, S. Tuecke, "The Anatomy of the Grid: Enabling Scalable Virtual Organizations", International Journal of Supercomputer Applications, 15 (3). 200--222. 2001.

Digital Library

[7]

G. Banga, P. Druschel, J. C. Mogul. "Resource Containers: A New Facility for Resource Management in Server Systems." Symposium on Operating Systems Design and Implementation, 1999.

Digital Library

[8]

J. A. Stankovic, K. Ramamritham, D. Niehaus, M. Humphrey, G. Wallace, "The Spring System: Integrated Support for Complex Real-Time Systems", Real-Time Systems, May 1999, Vol 16, No. 2/3, pp. 97--125.

Digital Library

[9]

J. Frey, T. Tannenbaum, I. Foster, M. Frey, S. Tuecke, "Condor-G: A Computation Management Agent for Multi-Institutional Grids," Cluster Computing, 2002.

Digital Library

[10]

G. Singh, C. Kesselman, E. Deelman, "Optimizing Grid-Based Workflow Execution." Journal of Grid Computing, Volume 3(3--4), December 2005, pp. 201--219.

[11]

E. Walker, J. P. Gardner, V. Litvin, E. L. Turner, "Creating Personal Adaptive Clusters for Managing Scientific Tasks in a Distributed Computing Environment", Workshop on Challenges of Large Applications in Distributed Environments, 2006.

[12]

G. Singh, C. Kesselman E. Deelman. "Performance Impact of Resource Provisioning on Workflows", USC ISI Technical Report 2006.

[13]

G. Mehta, C. Kesselman, E. Deelman. "Dynamic Deployment of VO-specific Schedulers on Managed Resources," USC ISI Technical Report, 2006.

[14]

D. Thain, T. Tannenbaum, and M. Livny, "Condor and the Grid", Grid Computing: Making The Global Infrastructure a Reality, John Wiley, 2003. ISBN: 0-470-85319-0.

[15]

E. Robinson, D. J. DeWitt. "Turning Cluster Management into Data Management: A System Overview", Conference on Innovative Data Systems Research, 2007.

[16]

B. Bode, D. M. Halstead, R. Kendall, Z. Lei, W. Hall, D. Jackson. "The Portable Batch Scheduler and the Maui Scheduler on Linux Clusters", Usenix, 4th Annual Linux Showcase & Conference, 2000.

Digital Library

[17]

S. Zhou. "LSF: Load sharing in large-scale heterogeneous distributed systems," Workshop on Cluster Computing, 1992.

[18]

W. Gentzsch, "Sun Grid Engine: Towards Creating a Compute Power Grid," 1st International Symposium on Cluster Computing and the Grid, 2001.

Digital Library

[19]

D. P. Anderson. "BOINC: A System for Public-Resource Computing and Storage." 5th IEEE/ACM International Workshop on Grid Computing, 2004.

Digital Library

[20]

D. P. Anderson, E. Korpela, R. Walton. "High-Performance Task Distribution for Volunteer Computing." IEEE Conference on e-Science and Grid Technologies, 2005.

Digital Library

[21]

The Functional Magnetic Resonance Imaging Data Center, http://www.fmridc.org/, 2007.

[22]

G. B. Berriman, et al., "Montage: a Grid Enabled Engine for Delivering Custom Science-Grade Image Mosaics on Demand." SPIE Conference on Astronomical Telescopes and Instrumentation. 2004.

[23]

K. Appleby, S. Fakhouri, L. Fong, G. Goldszmidt, M. Kalantar, S. Krishnakumar, D. Pazel, J. Pershing, and B. Rochwerger, "Oceano - SLA Based Management of a Computing Utility," 7th IFIP/IEEE International Symposium on Integrated Network Management, 2001.

[24]

L. Ramakrishnan, L. Grit, A. Iamnitchi, D. Irwin, A. Yumerefendi, J. Chase. "Toward a Doctrine of Containment: Grid Hosting with Adaptive Resource Control," IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC06), 2006.

Digital Library

[25]

J. Bresnahan. "An Architecture for Dynamic Allocation of Compute Cluster Bandwidth", MS Thesis, Department of Computer Science, University of Chicago, December 2006.

[26]

Catlett, C. et al., "TeraGrid: Analysis of Organization, System Architecture, and Middleware Enabling New Types of Applications," HPC 2006.

[27]

M. Feller, I. Foster, and S. Martin. "GT4 GRAM: A Functionality and Performance Study", TeraGrid Conference 2007.

[28]

I. Foster, "Globus Toolkit Version 4: Software for Service-Oriented Systems," Conference on Network and Parallel Computing, 2005.

Digital Library

[29]

The Globus Security Team. "Globus Toolkit Version 4 Grid Security Infrastructure: A Standards Perspective," Technical Report, Argonne National Laboratory, MCS, 2005.

[30]

I. Raicu, I. Foster, A. Szalay. "Harnessing Grid Resources to Enable the Dynamic Analysis of Large Astronomy Datasets", IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC06), 2006.

Digital Library

[31]

I. Raicu, I. Foster, A. Szalay, G. Turcu. "AstroPortal: A Science Gateway for Large-scale Astronomy Data Analysis", TeraGrid Conference 2006.

[32]

J. C. Jacob, et al. "The Montage Architecture for Grid-Enabled Science Processing of Large, Distributed Datasets." Earth Science Technology Conference 2004.

[33]

E. Deelman, et al. "Pegasus: a Framework for Mapping Complex Scientific Workflows onto Distributed Systems", Scientific Programming Journal, Vol 13(3), 2005, pp. 219--237.

Digital Library

[34]

T. Tannenbaum. "Condor RoadMap", Condor Week 2007.

[35]

K. Ranganathan, I. Foster, "Simulation Studies of Computation and Data Scheduling Algorithms for Data Grids", Journal of Grid Computing, V1(1) 2003.

Cited By

Kerney JRaicu IRaicu JChard K(2024)Towards Fine-Grained Parallelism in Parallel and Distributed Python Libraries2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00133(706-715)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00133
Liu ZKettimuthu RPapka MFoster I(2023)FreeTrain: A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid57682.2023.00036(299-310)Online publication date: May-2023
https://doi.org/10.1109/CCGrid57682.2023.00036
Jiang JHuang DChen HLu YLiao X(2023)HTDcr: a job execution framework for high-throughput computing on supercomputersScience China Information Sciences10.1007/s11432-022-3657-367:1Online publication date: 22-Dec-2023
https://doi.org/10.1007/s11432-022-3657-3
Show More Cited By

Index Terms

Falkon: a Fast and Light-weight tasK executiON framework
1. Software and its engineering
  1. Software creation and management
    1. Designing software
  2. Software organization and properties
    1. Contextual software domains
      1. Operating systems

Recommendations

A Data-Intensive Workflow Scheduling Algorithm for Grid Computing
CHINAGRID '09: Proceedings of the 2009 Fourth ChinaGrid Annual Conference

The data-intensive workflow in scientific and enterprise grids has gained popularity in recent times. Data-intensive workflow needs to access, process and transfer large datasets that may each be replicated on different data hosts. Because of the large ...
Specification and runtime workflow support in the ASKALON Grid environment
Dynamic Computational Workflows: Discovery, Optimization and Scheduling

We describe techniques to support the runtime execution of scientific workflows in the ASKALON Grid environment. We present a formal model and three middleware services that support in combination the effective execution in heterogeneous and dynamic ...
Easy distributed grid architecture for research: easy access to supercomputing

Current distributed systems present many challenges for students who may not be very skilled at programming parallel applications for use on such systems. Grid computing is a cost effective means of providing supercomputing computation for both ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SC '07: Proceedings of the 2007 ACM/IEEE conference on Supercomputing

November 2007

723 pages

ISBN:9781595937643

DOI:10.1145/1362622

General Chair:
Becky Verastegui
Oak Ridge National Laboratory

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

SC '07

Sponsor:

SIGARCH
IEEE-CS

SC '07: International Conference for High Performance Computing, Networking, Storage and Analysis

November 10 - 16, 2007

Nevada, Reno

Acceptance Rates

SC '07 Paper Acceptance Rate 54 of 268 submissions, 20%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

206
Total Citations
View Citations
550
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kerney JRaicu IRaicu JChard K(2024)Towards Fine-Grained Parallelism in Parallel and Distributed Python Libraries2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00133(706-715)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00133
Liu ZKettimuthu RPapka MFoster I(2023)FreeTrain: A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid57682.2023.00036(299-310)Online publication date: May-2023
https://doi.org/10.1109/CCGrid57682.2023.00036
Jiang JHuang DChen HLu YLiao X(2023)HTDcr: a job execution framework for high-throughput computing on supercomputersScience China Information Sciences10.1007/s11432-022-3657-367:1Online publication date: 22-Dec-2023
https://doi.org/10.1007/s11432-022-3657-3
Przybylski BPawlik MZuk PŁagosz BMalawski MRzadca KWolf FShende SCulhane CAlam SJagode H(2022)Using unusedProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.5555/3571885.3571938(1-15)Online publication date: 13-Nov-2022
https://dl.acm.org/doi/10.5555/3571885.3571938
Usman SMehmood RKatib IAlbeshri A(2022)Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System ArchitectureElectronics10.3390/electronics1201005312:1(53)Online publication date: 23-Dec-2022
https://doi.org/10.3390/electronics12010053
Hossain MHwang SKim J(2022)Resource Profiling and Performance Modeling for Distributed Scientific Computing EnvironmentsApplied Sciences10.3390/app1209479712:9(4797)Online publication date: 9-May-2022
https://doi.org/10.3390/app12094797
Merzky ATurilli MTitov MAl-Saadi AJha S(2022)Design and Performance Characterization of RADICAL-Pilot on Leadership-Class PlatformsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.310599433:4(818-829)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TPDS.2021.3105994
Przybylski BPawlik MŻuk PLagosz BMalawski MRzadca K(2022)Using Unused: Non-Invasive Dynamic FaaS Infrastructure with HPC-WhiskSC22: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41404.2022.00045(1-15)Online publication date: Nov-2022
https://doi.org/10.1109/SC41404.2022.00045
Hwang EKim JChoi Y(2021)Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed SystemsIEEE Transactions on Services Computing10.1109/TSC.2018.283644414:3(639-653)Online publication date: 1-May-2021
https://doi.org/10.1109/TSC.2018.2836444
Старовойтенко Олексій Володимирович (2020)ДОСЯГНЕННЯ ЕФЕКТИВНОГО РОЗПОДІЛЕНОГО ПЛАНУВАННЯ ЗА ДОПОМОГОЮ ЧЕРГ ПОВІДОМЛЕНЬ У ХМАРІ ДЛЯ БАГАТОЗАДАЧНИХ ОБЧИСЛЕНЬ ТА ВИСОКОПРОДУКТИВНИХ ОБЧИСЛЕНЬInternational Academy Journal Web of Scholar10.31435/rsglobal_wos/30122020/7323Online publication date: 19-Dec-2020
https://doi.org/10.31435/rsglobal_wos/30122020/7323
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents